Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time

Zhang, Chenyi; Liang, Hongwei; Wang, Ke; Sun, Jun

doi:10.1145/2806416.2806558

Cited by 56 publications

(29 citation statements)

References 31 publications

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“…In [26], the system solicits walking travel related attributes from tourists to insert concrete walking routes into POI itineraries, thereby supporting more experiential exploration of tourist destinations. Zhang et al [27,28] studied tour recommendation with the goal of recommending personalized itineraries based on the interest preferences of users and available touring time, while considering opening hours of POIs and uncertainty in travelling time. Other studies consider more practical factors that raise novel optimization challenges incorporating forms of situational awareness such as multiple modes of transport [29], considering traffic conditions [30][31][32], POI crowdedness [33,34], and queuing times [35].…”

Section: Related Workmentioning

confidence: 99%

A Travel Route Recommendation System Based on Smart Phones and IoT Environment

Bin

Sun

et al. 2019

Wireless Communications and Mobile Computing

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Tourism recommendation systems play a vital role in providing useful travel information to tourists. However, existing systems rarely aim at recommending tangible itineraries for tourists within a specific POI due to their lack of onsite travel behavioral data and related route mining algorithms. To this end, a novel travel route recommendation system is proposed, which collects tourist onsite travel behavior data automatically regarding a specific POI based on smart phone and IoT technology. Then, the proposed system preprocesses the behavior data to transform raw behavior sequences into Tourist-Behavior pattern sequences. Subsequently, the system discovers frequent travel routes from the generated pattern sequences by using an original route mining algorithm, named Tourist-Behavior PrefixSpan. Finally, a route-recommending method is designed to search and rank tangible travel routes according to the querying tourist's profile and constraint. The experimental results demonstrate that the proposed system is efficient and effective in recommending POI-oriented tangible travel routes considering tourists' route constraints and personal profile while ensuring that the suggested routes have considerable route values.

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Section: Related Workmentioning

confidence: 99%

A Travel Route Recommendation System Based on Smart Phones and IoT Environment

Bin

Sun

et al. 2019

Wireless Communications and Mobile Computing

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“…The works presented in this state of the art recommends sequences of POIs according to two technical types: (1) the construction of an optimal POIs path according to constraints coming from the field of operational research [37] and (2) calculating the probability of transition from one POI to another [40]. However, these techniques do not take into account neither the evolution of the number of POIs during the visit nor the users without profiles that feed the browsing history.…”

Section: Related Workmentioning

confidence: 99%

Towards an Incremental Recommendation of POIs for Mobile Tourists without Profiles

Dennouni

Peter

Lancieri

et al. 2018

IJISA

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Mobile tourism or m-tourism can assist and help tourists anywhere and anytime face the overload of information that may appear in their smartphones. Indeed, these mobile users find difficulties in the choice of points of interest (POIs) that may interest them during their discovery of a new environment (a city, a university campus ...). In order to reduce the number of POIs to visit, the recommendation systems (RS) represent a good solution to guide each tourist towards personalized paths close to his instantaneous location during his visit. In this article, we focus on (1) the detection of the spatiotemporal context of the tourist to filter the POIs and (2) the use of the previous notations of the places. These two criteria make it possible to integrate the evolutionary context of the visit in order to predict incrementally the most relevant transitions to be borrowed by the tourists without profile. These predictions are calculated using collaborative filtering algorithms that require the collection of traces of tourists to better refine the recommendation of POIs. In our software prototype, we have adapted the SLOPE ONE algorithm to our context of discovering the city of Chlef to avoid problems like data scarcity, cold start and scalability. In order to validate the use of this prototype, we conducted experiments by tourists in order to calculate indicators to assess the relevance of the recommendations provided by our system.

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“…This article is primarily extended from the previous work [Zhang et al 2015a]. One major addition is that the POI diversity constraint is considered in this work by allowing users to specify a threshold on the minimum number of categories of POIs that the recommended trip route covers.…”

Section: Travel Package Recommendationmentioning

confidence: 99%

Trip Recommendation Meets Real-World Constraints

Zhang

Liang

Wang

2016

ACM Trans. Inf. Syst.

Self Cite

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As location-based social network (LBSN) services become increasingly popular, trip recommendation that recommends a sequence of points of interest (POIs) to visit for a user emerges as one of many important applications of LBSNs. Personalized trip recommendation tailors to users' specific tastes by learning from past check-in behaviors of users and their peers. Finding the optimal trip that maximizes user's experiences for a given time budget constraint is an NP-hard problem and previous solutions do not consider three practical and important constraints. One constraint is POI availability, where a POI may be only available during a certain time window. Another constraint is uncertain traveling time, where the traveling time between two POIs is uncertain. In addition, the diversity of the POIs included in the trip plays an important role in user's final adoptions. This work presents efficient solutions to personalized trip recommendation by incorporating these constraints and leveraging them to prune the search space. We evaluated the efficiency and effectiveness of our solutions on real-life LBSN datasets.

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Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time

Cited by 56 publications

References 31 publications

A Travel Route Recommendation System Based on Smart Phones and IoT Environment

A Travel Route Recommendation System Based on Smart Phones and IoT Environment

Towards an Incremental Recommendation of POIs for Mobile Tourists without Profiles

Trip Recommendation Meets Real-World Constraints

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